Untitled

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0001.nii'));

spm('Defaults','fMRI')

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel/sub-0013/con_0001.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 39535452 bytes Loading image number: 99 Elapsed time is 315.393796 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 9333011 Bit rate: 23.15 bits

contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...

'stim_P', 'stim_V', 'stim_C', 'stim_G',...

'motor', ...

'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...

'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans'); % display

SPM12: spm_check_registration (v7759) 11:59:57 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 37.37% Expected 4.95 outside 95% ellipsoid, found 7 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 65 Uncorrected: 7 images Cases 17 26 27 61 65 75 81 Retained 10 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 45.45% Expected 4.95 outside 95% ellipsoid, found 3 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 3 images Cases 25 33 46 Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 3 3.0303 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.0101 mahal_cov_uncor 7 7.0707 mahal_cov_corrected 1 1.0101 mahal_corr_uncor 3 3.0303 mahal_corr_corrected 0 0 Overall_uncorrected 10 10.101 Overall_corrected 1 1.0101

SPM12: spm_check_registration (v7759) 12:00:55 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 99

%for s = 1:length(wh_outlier_corr)

%disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 1 participants, size is now 98

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

participants that are outliers:... sub-0091

disp(n);

sub-0091

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 12:01:01 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.032480 Image 1 32 contig. clusters, sizes 1 to 64203 Positive effect: 56856 voxels, min p-value: 0.00000000 Negative effect: 8004 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 12:01:03 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .001, 'fdr');

Image 1 FDR q < 0.001 threshold is 0.000433 Image 1 49 contig. clusters, sizes 1 to 41119 Positive effect: 39741 voxels, min p-value: 0.00000000 Negative effect: 3494 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 12:01:05 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 1222 voxels displayed, 42013 not displayed on these slices

sagittal montage: 1204 voxels displayed, 42031 not displayed on these slices

sagittal montage: 1190 voxels displayed, 42045 not displayed on these slices

axial montage: 8620 voxels displayed, 34615 not displayed on these slices

axial montage: 8980 voxels displayed, 34255 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( m, 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ ________________ -0.13291 {'frequency' } 0.20129 {'counting' } -0.13081 {'images' } 0.19871 {'preparatory' } -0.10573 {'age' } 0.19543 {'autistic' } -0.10449 {'trait' } 0.18544 {'anticipatory'} -0.10042 {'time' } 0.18476 {'1back' } -0.095454 {'risk' } 0.18441 {'effort' } -0.093013 {'approach' } 0.18374 {'grammatical' } -0.090584 {'disorder' } 0.18289 {'phonology' } -0.089781 {'pair' } 0.18266 {'colour' } -0.082778 {'association'} 0.18262 {'retention' }

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0531 -6.9820 0.0000 1.0000 Cog Wholebrain 0.0107 3.1150 0.0024 1.0000 Emo Wholebrain 0.0407 6.4046 0.0000 1.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.053143 0.0075957 -6.9965 3.2675e-10 -0.70318 {'Cog Wholebrain' } 0.010678 0.0034288 3.1143 0.0024178 0.313 {'Emo Wholebrain' } 0.040745 0.0063517 6.4149 5.0033e-09 0.64472

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {99×3 cell} text_han: {99×3 cell} point_han: {99×3 cell} star_handles: [11.0001 12.0001 13.0001]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(con_data_obj, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.04833 0.0071502 -6.7592 1.0041e-09 -0.67933 {'Cog Wholebrain' } 0.011168 0.003199 3.491 0.00072304 0.35086 {'Emo Wholebrain' } 0.036032 0.00601 5.9954 3.3913e-08 0.60256

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {99×3 cell} text_han: {99×3 cell} point_han: {99×3 cell} star_handles: [14.0001 15.0001 16.0001]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0002.nii'));

spm('Defaults','fMRI');

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel/sub-0013/con_0002.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 39535452 bytes Loading image number: 99 Elapsed time is 143.433609 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 9340838 Bit rate: 23.16 bits

contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...

'stim_P', 'stim_V', 'stim_C', 'stim_G',...

'motor', ...

'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...

'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 12:07:42 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 5 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 38.38% Expected 4.95 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 65 95 Uncorrected: 8 images Cases 22 30 61 62 65 81 95 98 Retained 10 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 53.54% Expected 4.95 outside 95% ellipsoid, found 1 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 1 images Cases 64 Mahalanobis (cov and corr, q<0.05 corrected): 2 images Outlier_count Percentage _____________ __________ global_mean 1 1.0101 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 3 3.0303 mahal_cov_uncor 8 8.0808 mahal_cov_corrected 2 2.0202 mahal_corr_uncor 1 1.0101 mahal_corr_corrected 0 0 Overall_uncorrected 9 9.0909 Overall_corrected 3 3.0303

SPM12: spm_check_registration (v7759) 12:08:35 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 99

%for s = 1:length(wh_outlier_corr)

% disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 3 participants, size is now 96

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…"

disp(n);

{'sub-0051'} {'sub-0091'} {'sub-0129'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 12:08:42 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.025415 Image 1 50 contig. clusters, sizes 1 to 49535 Positive effect: 4971 voxels, min p-value: 0.00000000 Negative effect: 45776 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 12:08:43 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 1331 voxels displayed, 49416 not displayed on these slices

sagittal montage: 1418 voxels displayed, 49329 not displayed on these slices

sagittal montage: 1252 voxels displayed, 49495 not displayed on these slices

axial montage: 9709 voxels displayed, 41038 not displayed on these slices

axial montage: 10432 voxels displayed, 40315 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( m, 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ ________________ -0.13291 {'frequency' } 0.20129 {'counting' } -0.13081 {'images' } 0.19871 {'preparatory' } -0.10573 {'age' } 0.19543 {'autistic' } -0.10449 {'trait' } 0.18544 {'anticipatory'} -0.10042 {'time' } 0.18476 {'1back' } -0.095454 {'risk' } 0.18441 {'effort' } -0.093013 {'approach' } 0.18374 {'grammatical' } -0.090584 {'disorder' } 0.18289 {'phonology' } -0.089781 {'pair' } 0.18266 {'colour' } -0.082778 {'association'} 0.18262 {'retention' }

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain 0.0340 4.9396 0.0000 1.0000 Cog Wholebrain -0.0123 -3.4080 0.0010 1.0000 Emo Wholebrain -0.0212 -3.5744 0.0005 1.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} 0.033974 0.0068682 4.9465 3.1329e-06 0.49714 {'Cog Wholebrain' } -0.012276 0.0035996 -3.4104 0.00094359 -0.34275 {'Emo Wholebrain' } -0.021177 0.0059218 -3.5761 0.00054355 -0.35942

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {99×3 cell} text_han: {99×3 cell} point_han: {99×3 cell} star_handles: [11.0002 12.0002 13.0002]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(con_data_obj, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} 0.032472 0.0066174 4.9071 3.6826e-06 0.49318 {'Cog Wholebrain' } -0.0128 0.0034669 -3.692 0.00036592 -0.37106 {'Emo Wholebrain' } -0.019336 0.0058073 -3.3296 0.0012267 -0.33463

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {99×3 cell} text_han: {99×3 cell} point_han: {99×3 cell} star_handles: [14.0002 15.0002 16.0002]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0003.nii'));

spm('Defaults','fMRI');

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel/sub-0013/con_0003.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 39535452 bytes Loading image number: 99 Elapsed time is 113.446299 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 9338749 Bit rate: 23.15 bits

contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...

'stim_P', 'stim_V', 'stim_C', 'stim_G',...

'motor', ...

'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...

'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans'); % display

SPM12: spm_check_registration (v7759) 12:13:20 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 36.36% Expected 4.95 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 17 61 65 Uncorrected: 8 images Cases 1 17 19 30 61 65 75 95 Retained 11 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 46.46% Expected 4.95 outside 95% ellipsoid, found 2 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 2 images Cases 46 56 Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 2 2.0202 global_mean_to_variance 1 1.0101 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 3 3.0303 mahal_cov_uncor 8 8.0808 mahal_cov_corrected 3 3.0303 mahal_corr_uncor 2 2.0202 mahal_corr_corrected 0 0 Overall_uncorrected 10 10.101 Overall_corrected 4 4.0404

SPM12: spm_check_registration (v7759) 12:14:15 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 99

%for s = 1:length(wh_outlier_corr)

% disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 4 participants, size is now 95

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outl…" "participants that are outl…" "participants that are outl…" "participants that are outl…"

disp(n);

{'sub-0032'} {'sub-0087'} {'sub-0091'} {'sub-0129'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 12:14:21 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.024397 Image 1 63 contig. clusters, sizes 1 to 46162 Positive effect: 7454 voxels, min p-value: 0.00000000 Negative effect: 41275 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 12:14:23 - 10/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 1284 voxels displayed, 47445 not displayed on these slices

sagittal montage: 1230 voxels displayed, 47499 not displayed on these slices

sagittal montage: 1285 voxels displayed, 47444 not displayed on these slices

axial montage: 9528 voxels displayed, 39201 not displayed on these slices

axial montage: 10133 voxels displayed, 38596 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( m, 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ ________________ -0.13291 {'frequency' } 0.20129 {'counting' } -0.13081 {'images' } 0.19871 {'preparatory' } -0.10573 {'age' } 0.19543 {'autistic' } -0.10449 {'trait' } 0.18544 {'anticipatory'} -0.10042 {'time' } 0.18476 {'1back' } -0.095454 {'risk' } 0.18441 {'effort' } -0.093013 {'approach' } 0.18374 {'grammatical' } -0.090584 {'disorder' } 0.18289 {'phonology' } -0.089781 {'pair' } 0.18266 {'colour' } -0.082778 {'association'} 0.18262 {'retention' }

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0442 -13.0090 0.0000 1.0000 Cog Wholebrain 0.0005 0.1744 0.8619 0.0000 Emo Wholebrain 0.0414 11.9877 0.0000 1.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.044179 0.0033908 -13.029 2.2204e-15 -1.3095 {'Cog Wholebrain' } 0.00047053 0.0026949 0.1746 0.86175 0.017548 {'Emo Wholebrain' } 0.041386 0.0034489 12 2.2204e-15 1.206

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {99×3 cell} text_han: {99×3 cell} point_han: {99×3 cell} star_handles: [15.0055 16.0055 17.0055]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(con_data_obj, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.043382 0.0033672 -12.883 2.2204e-15 -1.2948 {'Cog Wholebrain' } 0.0024441 0.0025989 0.94044 0.3493 0.094518 {'Emo Wholebrain' } 0.039317 0.0034077 11.538 2.2204e-15 1.1596

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {99×3 cell} text_han: {99×3 cell} point_han: {99×3 cell} star_handles: [18.0055 19.0055 20.0055]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

drawnow, snapnow;

% save html

pubdir = pwd;

pubfilename = 's03_PVC_stim_contrasts_ROI.mlx';

p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...

'format', 'html', 'outputDir', pubdir, ...

'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));

htmlfile = publish(pubfilename, p);

Error using evalmxdom>instrumentAndRun (line 116)
Publishing a script that contains a publish function is not supported.

Error in evalmxdom (line 21)
[data,text,laste] = instrumentAndRun(file,cellBoundaries,imageDir,imagePrefix,options);

Error in publish

Pain > (Vicarious & Cognitive)

Pain > VC :: load dataset

Pain > VC :: check data coverage

Pain > VC :: Plot diagnostics, before l2norm

Pain > VC :: run robfit

Pain > VC :: remove outliers based on plot

Pain > VC :: plot diagnostics, after l2norm

Pain > VC :: ttest

Pain > VC :: Neurosynth similarity

Vicarious > (Pain & Cognitive)

Vicarious > PC :: load dataset

Vicarious > PC :: check data coverage

Vicarious > PC :: Plot diagnostics, before l2norm

Vicarious > PC :: run robfit

Vicarious > PC :: remove outliers based on plot

Vicarious > PC :: plot diagnostics, after l2norm

Vicarious > PC :: ttest

Vicarious > PC :: Neurosynth similarity

Cognitive > (Pain & Vicarious)

Cognitive > PV :: load dataset

Cognitive > PV :: check data coverage

Cognitive > PV :: Plot diagnostics, before l2norm

Cognitive > PV :: run robfit

Cognitive > PV :: remove outliers based on plot

Cognitive > PV:: plot diagnostics, after l2norm

Cognitive > PV :: ttest

Cognitive > PV :: Neurosynth similarity